Search results for: deep learning models

Authors: Zafar Iqbal

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This study involved students of 6th semester enrolled in a Bachelor of Computer Science Program at university level. In this era of science and technology, e-learning can be helpful for grassroots in providing them access to education tenant in less developed areas. It is a potential substitute of face-to-face teaching being used in different countries. The purpose of the study was to explore the views of students about e-learning (Facebook) as an instructional tool. By using purposive sampling technique an intact class of 30 students included both male and female were selected where e-learning was used as an instructional tool. The views of students were explored through qualitative approach by using focus group interviews. The approach was helpful to develop comprehensive understanding of students’ views towards e- learning. In addition, probing questions were also asked and recorded. Data was transcribed, generated nodes and then coded text against these nodes. For this purpose and further analysis, NVivo 10 software was used. Themes were generated and tangibly presented through cluster analysis. Findings were interesting and provide sufficient evidence that face book is a subsequent e-learning source for students of higher education. Students acknowledged it as best source of learning and it was aligned with their academic and social behavior. It was not time specific and therefore, feasible for students who work day time and can get on line access to the material when they got free time. There were some distracters (time wasters) reported by the students but can be minimized by little effort. In short, e-learning is need of the day and potential learning source for every individual who have access to internet living at any part of the globe.

Keywords: e-learning, facebook, instructional tool, higher education

Procedia PDF Downloads 374

Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid

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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.

Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming

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Authors: Celina Pestano-Gabino, Concepcion Gonzalez-Concepcion, M. Candelaria Gil-Fariña

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Some estimation methods for VARMA models, and Multivariate Time Series Models in general, rely on the use of a Hankel matrix. It is known that if the data sample is populous enough and the dimension of the Hankel matrix is unnecessarily large, this may result in an unnecessary number of computations as well as in numerical problems. In this sense, the aim of this paper is two-fold. First, we provide some theoretical results for these matrices which translate into a lower dimension for the matrices normally used in the algorithms. This contribution thus serves to improve those methods from a numerical and, presumably, statistical point of view. Second, we have chosen an estimation algorithm to illustrate in practice our improvements. The results we obtained in a simulation of VARMA models show that an increase in the size of the Hankel matrix beyond the theoretical bound proposed as valid does not necessarily lead to improved practical results. Therefore, for future research, we propose conducting similar studies using any of the linear system estimation methods that depend on Hankel matrices.

Keywords: covariances Hankel matrices, Kronecker indices, system identification, VARMA models

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Authors: Yafit Gabay

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Attention deficit hyperactivity disorder (ADHD) has been associated with both suboptimal functioning in the striatum and prefrontal cortex. Such abnormalities may impede the acquisition of perceptual categories, which are important for fundamental abilities such as object recognition and speech perception. Indeed, prior research has supported this possibility, demonstrating that children with ADHD have similar visual category learning performance as their neurotypical peers but use suboptimal learning strategies. However, much less is known about category learning processes in the auditory domain or among adults with ADHD in which prefrontal functions are more mature compared to children. Here, we investigated auditory and visual perceptual category learning in adults with ADHD and neurotypical individuals. Specifically, we examined learning of rule-based categories – presumed to be optimally learned by a frontal cortex-mediated hypothesis testing – and information-integration categories – hypothesized to be optimally learned by a striatally-mediated reinforcement learning system. Consistent with striatal and prefrontal cortical impairments observed in ADHD, our results show that across sensory modalities, both rule-based and information-integration category learning is impaired in adults with ADHD. Computational modeling analyses revealed that individuals with ADHD were slower to shift to optimal strategies than neurotypicals, regardless of category type or modality. Taken together, these results suggest that both explicit, frontally mediated and implicit, striatally mediated category learning are impaired in ADHD. These results suggest impairments across multiple learning systems in young adults with ADHD that extend across sensory modalities and likely arise from domain-general mechanisms.

Keywords: ADHD, category learning, modality, computational modeling

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Authors: Sari Myréen

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The aim of this study was to evaluate the role of multisensory elements in enhancing and facilitating foreign language acquisition among adult students in a language classroom. The use of multisensory elements enables the creation of a student-centered classroom, where the focus is on individual learner’s language learning process, perceptions and motivation. Multisensory language learning is a pedagogical approach where the language learner uses all the senses more effectively than in a traditional in-class environment. Language learning is facilitated due to multisensory stimuli which increase the number of cognitive connections in the learner and take into consideration different types of learners. A living lab called Multisensory Space creates a relaxed and receptive state in the learners through various multisensory stimuli, and thus promotes their natural foreign language acquisition. Qualitative and quantitative data were collected in two questionnaire inquiries among the Finnish students of a higher education institute at the end of their basic French courses in December 2014 and 2016. The inquiries discussed the effects of multisensory elements on the students’ motivation to study French as well as their learning outcomes. The results show that the French classes in the Multisensory Space provide the students with an encouraging and pleasant learning environment, which has a positive impact on their motivation to study the foreign language as well as their language learning outcomes.

Keywords: foreign language acquisition, pedagogical approach, multisensory learning, transcultural learning

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Authors: Yooseo Pang, Jongwoong Choi, Yong Cho, Yongchae Jeong

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Climate crisis, such as environmental problems related to energy supply, is getting emerged issues, so the use of renewable energy is essentially required to solve these problems, which are mainly managed by the Paris Agreement, the international treaty on climate change. The government of the Republic of Korea announced that the key long-term goal for a low-carbon strategy is “Carbon neutrality by 2050”. It is focused on the role of the internet data centers (IDC) in which large amounts of data, such as artificial intelligence (AI) and big data as an impact of the 4th industrial revolution, are managed. The demand for the cooling system market for IDC was about 9 billion US dollars in 2020, and 15.6% growth a year is expected in Korea. It is important to control the temperature in IDC with an efficient air conditioning system, so hydrothermal energy is one of the best options for saving energy in the cooling system. In order to save energy and optimize the operating conditions, it has been considered to apply ‘the dam deep water air conditioning system. Deep water at a specific level from the dam can supply constant water temperature year-round. It will be tested & analyzed the amount of energy saving with a pilot plant that has 100RT cooling capacity. Also, a target of this project is 1.2 PUE (Power Usage Effectiveness) which is the key parameter to check the efficiency of the cooling system.

Keywords: hydrothermal energy, HVAC, internet data center, free-cooling

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Authors: Ogoh Andrew Enokela

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This paper dwelt on the need for the utilization of instructional materials with highlights on the type of instructional materials, selection, uses and their importance on the learning and teaching of Agricultural Science Education in developing countries. It further discussed the concept of improvisation with some recommendation in terms of availability, utilization on the teaching and learning of Agricultural Science Education.

Keywords: instructional materials, agricultural science education, improvisation, teaching and learning

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Authors: Milan Joshi, Harsh Agrawal, Pallaw Mishra, Sanand Sule

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Predicting electricity usage is a crucial aspect of organizing and controlling sustainable energy systems. The task of forecasting electricity load is intricate and requires a lot of effort due to the combined impact of social, economic, technical, environmental, and cultural factors on power consumption in communities. As a result, it is important to create strong models that can handle the significant non-linear and complex nature of the task. The objective of this study is to create and compare three machine learning techniques for predicting electricity load for both the day ahead and intraday, taking into account various factors such as meteorological data and social events including holidays and festivals. The proposed methods include a LightGBM, FBProphet, combination of FBProphet and LightGBM for day ahead and Motifs( Stumpy) based on Mueens algorithm for similarity search for intraday. We utilize these techniques to predict electricity usage during normal days and social events in the Himachal Region. We then assess their performance by measuring the MSE, RMSE, and MAPE values. The outcomes demonstrate that the combination of FBProphet and LightGBM method is the most accurate for day ahead and Motifs for intraday forecasting of electricity usage, surpassing other models in terms of MAPE, RMSE, and MSE. Moreover, the FBProphet - LightGBM approach proves to be highly effective in forecasting electricity load during social events, exhibiting precise day ahead predictions. In summary, our proposed electricity forecasting techniques display excellent performance in predicting electricity usage during normal days and special events in the Himachal Region.

Keywords: feature engineering, FBProphet, LightGBM, MASS, Motifs, MAPE

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Authors: Songul Cinaroglu

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Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures.

Keywords: machine learning, lasso regression, random forest regression, support vector regression, hyperparameter tuning, health expenditure

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Authors: Ting Hsiang Chang

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Nowadays in Taiwan, higher education, which was previously more emphasized on teaching-oriented approaches, has gradually shifted to an approach more focusing on students learning outcomes. With student employment rate as an important indicator for University Program Evaluation periodically held by the Ministry of Education, it becomes extremely critical for a university to build up a teaching and learning quality assurance system to bridge the gap between learning and practice. Teaching and Learning Quality Assurance System has been built and implemented at Chaoyang University of Technology for years and has received substantial results. By employing various forms of evaluation and performance appraisals, the effectiveness of teaching and learning can consistently be tracked as a means of ensuring teaching and learning quality. This study aims to explore the evaluation system of teaching and learning quality assurance system at the Chaoyang University of Technology by means of content analysis. The study contents the evaluation reports on the teaching and learning quality assurance at the Chaoyang University of Technology in the Academic Year 2013-2015. The quantitative results of the assessment were analyzed using the five-point Likert Scale. Quality assurance Committee meetings were further held for examining and discussions on the results. To the end, the annual evaluation report is to be produced as references used to improve approaches in both teaching and learning. The findings indicate that there is a respective relationship between the overall teaching evaluation items and the teaching goals and core competencies. In addition, graduates’ feedbacks were also collected for further analysis to examine if the current educational planning is able to achieve the university’s teaching goal and cultivation of core competencies.

Keywords: core competencies, teaching and learning quality assurance system, teaching goals, university program evaluation

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Abdul Rashid Ahmadi, Samiullah Paracha, Hamidullah Sokout, Mohammad Hanif Gharana

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Conventional educational practices, do not offer all the required skills for teachers to successfully survive in today’s workplace. Due to poor professional training, a big gap exists across the curriculum plan and the teacher practices in the classroom. As such, raising the quality of teaching through ICT-enabled training and professional development of teachers should be an urgent priority. ‘Mobile Learning’, in that vein, is an increasingly growing field of educational research and practice across schools and work places. In this paper, we propose a novel Mobile learning system that allows the users to learn through an intelligent mobile learning in cooperatively every-time and every-where. The system will reduce the training cost and increase consistency, efficiency, and data reliability. To establish that our system will display neither functional nor performance failure, the evaluation strategy is based on formal observation of users interacting with system followed by questionnaires and structured interviews.

Keywords: computer assisted learning, intelligent tutoring system, learner centered design, mobile mediated learning and teacher education

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Authors: Mukhtar Raban

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Nelson Mandela University subscribes to a humanizing pedagogy (HP), as housed under broader critical pedagogy, that underpins and informs learning and teaching activities at the institution. The investigation sought to explore the implications of humanizing and critical pedagogical considerations for a technology-enhanced language learning (TELL) environment in a university course. The paper inquires into the design of a learning resource in an online learning environment of an English communication module, that applied HP principles. With an objective of creating agentive spaces for foregrounding identity, student voice, critical self-reflection, and recognition of others’ humanity; a flexible and open 'My Presence' feature was added to the TELL environment that allowed students and lecturers to share elements of their backgrounds in a ‘mutually vulnerable’ manner as a way of establishing digital identity and a more ‘human’ presence in the online language learning encounter, serving as a catalyst for the recognition of the ‘other’. Following a qualitative research design, the study adopted an auto-ethnographic approach, complementing the critical inquiry nature embedded into the activity’s practices. The study’s findings provide critical reflections and deductions on the possibilities of leveraging digital human expression within a humanizing pedagogical framework to advance the realization of HP-adoption in language learning and teaching encounters. It was found that the consideration of humanizing pedagogical principles in the design of online learning was more effective when the critical outcomes were explicated to students and lecturers prior to the completion of the activities. The integration of humanizing pedagogy also led to a contextual advancement of ‘affective’ language learning. Upon critical reflection and analysis, student identity and agency can flourish in a technology-enhanced learning environment when humanizing, and critical pedagogy influences the learning design.

Keywords: critical reflection, humanizing pedagogy, student identity, technology-enhanced language learning

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Authors: Yessica A. Aguilera

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English is currently the only foreign language included in the national educational curriculum in Chile. The English curriculum establishes that once completed secondary education, students are expected to reach B1 level according to the Common European Reference Framework (CEFR) scale. However, the objective has not been achieved, and to the author’s best knowledge, there is still a severe lack of English language skills among students who have completed their secondary education studies. In order to deal with the fact that students do not manage English as expected, team-based learning (TBL) was introduced in English language lessons at the Universidad Federico Santa María (USM). TBL is a collaborative teaching-learning method which enhances active learning by combining individual and team work. This approach seeks to help students achieve course objectives while learning how to function in teams. The purpose of the research was to assess the implementation and effectiveness of TBL in English language classes at USM technical training education. Quantitative and qualitative data were collected from teachers and students about their experience through TBL. Research findings show that both teachers and students are satisfied with the method and that students’ engagement and participation in class is higher. Additionally, students score higher on examinations improving academic outcomes. The findings of the research have the potential to guide how TBL could be included in future English language courses.

Keywords: collaborative learning, college education, English language learning, team-based learning

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Authors: Josua Sihotang

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Jakarta Capital Special Region is the province that densely populated with rapidly growing infrastructure but less attention for the environmental condition. This makes some social problem happened like lack of clean water supply. Shallow groundwater and river water condition that has contaminated make the layer of deep water carrier (aquifer) should be done. This research aims to provide the people insight about deep groundwater potential and to determine the depth, location, and quality where the aquifer can be found in Jakarta’s area, particularly Kapuk-Cengkareng’s people. This research was conducted by geophysical method namely Well Logging Analysis. Well Logging is the geophysical method to know the subsurface lithology with the physical characteristic. The observation in this research area was conducted with several well devices that is Spontaneous Potential Log (SP Log), Resistivity Log, and Gamma Ray Log (GR Log). The first devices well is SP log which is work by comprising the electrical potential difference between the electrodes on the surface with the electrodes that is contained in the borehole and rock formations. The second is Resistivity Log, used to determine both the hydrocarbon and water zone based on their porosity and permeability properties. The last is GR Log, work by identifying radioactivity levels of rocks which is containing elements of thorium, uranium, or potassium. The observation result is curve-shaped which describes the type of lithological coating in subsurface. The result from the research can be interpreted that there are four of the deep groundwater layer zone with different quality. The good groundwater layer can be found in layers with good porosity and permeability. By analyzing the curves, it can be known that most of the layers which were found in this wellbore are clay stone with low resistivity and high gamma radiation. The resistivity value of the clay stone layers is about 2-4 ohm-meter with 65-80 Cps gamma radiation. There are several layers with high resistivity value and low gamma radiation (sand stone) that can be potential for being an aquifer. This is reinforced by the sand layer with a right-leaning SP log curve proving that this layer is permeable. These layers have 4-9 ohm-meter resistivity value with 40-65 Cps gamma radiation. These are mostly found as fresh water aquifer.

Keywords: aquifer, deep groundwater potential, well devices, well logging analysis

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Authors: Wala A. Bagunaid, Maram Meccawy, Arwa Allinjawi, Zilal Meccawy

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Continuous self-assessment becomes crucial in self-paced online learning environments. Students often depend on themselves to assess their progress; which is considered an essential requirement for any successful learning process. Today’s education institutions face major problems around student motivation and engagement. Thus, personalized e-learning systems aim to help and guide the students. Gamification provides an opportunity to help students for self-assessment and social comparison with other students through attempting to harness the motivational power of games and apply it to the learning environment. Furthermore, Open Social Student Modeling (OSSM) as considered as the latest user modeling technologies is believed to improve students’ self-assessment and to allow them to social comparison with other students. This research integrates OSSM approach and gamification concepts in order to provide self-assessment for English language learners at King Abdulaziz University (KAU). This is achieved through an interactive visual representation of their learning progress.

Keywords: e-learning system, gamification, motivation, social comparison, visualization

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Authors: I. Sagaama, A. Kechiche, W. Trojet, F. Kamoun

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Replacing combustion engine vehicles by electric vehicles (EVs) is a major step in recent years due to their potential benefits. Battery autonomy and charging processes are still a big issue for that kind of vehicles. Therefore, reducing the energy consumption of electric vehicles becomes a necessity. Many researches target introducing recent information and communication technologies in EVs in order to propose reducing energy consumption services. Evaluation of realistic scenarios is a big challenge nowadays. In this paper, we will elaborate a state of the art of different proposed energy consumption models in the literature, then we will present a comparative study of these models, finally, we will extend previous works in order to propose an accurate and realistic energy model for calculating instantaneous power consumption of electric vehicles.

Keywords: electric vehicle, vehicular networks, energy models, traffic simulation

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Authors: Julio Manuel De Luis Ruiz, Javier Sedano Cibrián, RubéN Pérez Álvarez, Raúl Pereda García, Felipe Piña García

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Nowadays it is common to resort to aerial photography to carry out the prospection and/or exploration of archaeological sites. In this sense, the classic 3D models are being applied to investigate the direction towards which the generally subterranean structures of an archaeological site may continue and therefore, to help in making the decisions that define the location of new excavations. In recent years, Unmanned Aerial Vehicles (UAVs) have been applied as the vehicles that carry the sensor. This implies certain advantages, such as the possibility of including low-cost sensors, given that these vehicles can carry the sensor at relatively low altitudes. Due to this, low-cost dual sensors have recently begun to be used. This new equipment can collaborate with classic Digital Elevation Models (DEMs) in the exploration of archaeological sites, but this entails the need for a methodological setting to optimise the acquisition, processing and exploitation of the information provided by low-cost dual sensors. This research focuses on the design of an appropriate workflow to obtain 3D models with low-cost sensors carried on UAVs, both in the RGB and thermal domains. All the foregoing has been applied to the archaeological site of Juliobriga, located in Cantabria (Spain).

Keywords: process optimization, RGB models, thermal models, , UAV, workflow

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Authors: Glemarie C. Hermosa, Sheng-Jie You, Chien Chih Hu

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Efficient separation technologies are required for the removal of carbon dioxide from natural gas streams. Membrane-based natural gas separation has emerged as one of the fastest growing technologies, due to the compactness, higher energy efficiency and economic advantages which can be reaped. The removal of Carbon dioxide from gas streams using membrane technology will also give the advantage like environmental friendly process compared to the other technologies used in gas separation. In this study, Polyimide membranes, which are mostly used in the separation of gases, are blended with a new kind of solvent: Deep Eutectic Solvents or simply DES. The three types of DES are used are choline chloride based mixed with three different hydrogen bond donors: Lactic acid, N-methylurea and Urea. The blending of the DESs to Polyimide gave out high permeability performance. The Gas Separation performance for all the membranes involving CO2/CH4 showed low performance while for CO2/N2 surpassed the performance of some studies. Among the three types of DES used the solvent Choline Chloride/Lactic acid exhibited the highest performance for both Gas Separation applications. The values are 10.5 for CO2/CH4 selectivity and 60.5 for CO2/N2. The separation results for CO2/CH4 may be due to the viscosity of the DESs affecting the morphology of the fabricated membrane thus also impacts the performance. DES/blended Polyimide membranes fabricated are novel and have the potential of a low-cost and environmental friendly application for gas separation.

Keywords: deep eutectic solvents, gas separation, polyimide blends, polyimide membranes

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Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Ahmed Amin Mousa, M. Abd El-Salam

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This study discusses a simple solution for the problem of shortage in learning resources for kindergarten teachers. Occasionally, kindergarten teachers cannot access proper resources by usual search methods as libraries or search engines. Furthermore, these methods require a long time and efforts for preparing. The study is expected to facilitate accessing learning resources. Moreover, it suggests a potential direction for using QR code inside the classroom. The present work proposes that QR code can be used for digitizing kindergarten curriculums and accessing various learning resources. It investigates using QR code for saving information related to the concepts which kindergarten teachers use in the current educational situation. The researchers have established a guide for kindergarten teachers based on the Egyptian official curriculum. The guide provides different learning resources for each scientific and mathematical concept in the curriculum, and each learning resource is represented as a QR code image that contains its URL. Therefore, kindergarten teachers can use smartphone applications for reading QR codes and displaying the related learning resources for students immediately. The guide has been provided to a group of 108 teachers for using inside their classrooms. The results showed that the teachers approved the guide, and gave a good response.

Keywords: kindergarten, child, learning resources, QR code, smart phone, mobile

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Authors: Anli Du Preez, James Bekker

Abstract:

Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Peter Adekunle, Clinton Aigbavboa, Matthew Ikuabe, Opeoluwa Akinradewo

Abstract:

The future of machine learning (ML) in building may seem like a distant idea that will take decades to materialize, but it is actually far closer than previously believed. In reality, the built environment has been progressively increasing interest in machine learning. Although it could appear to be a very technical, impersonal approach, it can really make things more personable. Instead of eliminating humans out of the equation, machine learning allows people do their real work more efficiently. It is therefore vital to evaluate the factors influencing the implementation and challenges of implementing machine learning techniques in the South African built environment. The study's design was one of a survey. In South Africa, construction workers and professionals were given a total of one hundred fifty (150) questionnaires, of which one hundred and twenty-four (124) were returned and deemed eligible for study. Utilizing percentage, mean item scores, standard deviation, and Kruskal-Wallis, the collected data was analyzed. The results demonstrate that the top factors influencing the adoption of machine learning are knowledge level and a lack of understanding of its potential benefits. While lack of collaboration among stakeholders and lack of tools and services are the key hurdles to the deployment of machine learning within the South African built environment. The study came to the conclusion that ML adoption should be promoted in order to increase safety, productivity, and service quality within the built environment.

Keywords: machine learning, implementation, built environment, construction stakeholders

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Authors: Waraporn Thaimit, Yuwadee Insamran, Natchanok Jansawang

Abstract:

Focusing on Analytical Thinking and Learning Achievement are the critical component of visual thinking that gives one the ability to solve problems quickly and effectively that allows to complex problems into components, and the result had been achieved or acquired form of the subject students of which resulted in changes within the individual as a result of activity in learning. The aims of this study are to administer on comparisons between students’ analytical thinking abilities and their learning achievements sample size consisted of 80 students who sat at the 12th grade level in 2 classes from Chaturaphak Phiman Ratchadaphisek School, the 40-student experimental group with the Problem-Based Learning (PBL) and 40-student controlling group with the Concept Mapping Instructional (CMI) methods were designed. Research instruments composed with the 5-lesson instructional plans to be assessed with the pretest and posttest techniques on each instructional method. Students’ responses of their analytical thinking abilities were assessed with the Analytical Thinking Tests and students’ learning achievements were tested of the Learning Achievement Tests. Statistically significant differences with the paired t-test and F-test (Two-way MANCOVA) between post- and pre-tests of the whole students in two chemistry classes were found. Associations between student learning outcomes in each instructional method and their analytical thinking abilities to their learning achievements also were found (ρ < .05). The use of two instructional methods for this study is revealed that the students perceive their abilities to be highly learning achievement in chemistry classes with the PBL group ought to higher than the CMI group. Suggestions that analytical thinking ability involves the process of gathering relevant information and identifying key issues related to the learning achievement information.

Keywords: comparisons, students learning achievements, analytical thinking abilities, the problem-based learning method, the concept mapping instructional method, gene and chromosome issue, chemistry classes

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Authors: Arcady Ponosov

Abstract:

Many well-known age-structured population models are derived from the celebrated McKendrick-von Foerster equation (MFE), also called the biological conservation law. A similar technique is suggested for the stochastically perturbed MFE. This technique is shown to produce stochastic versions of the deterministic population models, which appear to be very different from those one can construct by simply appending additive stochasticity to deterministic equations. In particular, it is shown that stochastic Nicholson’s blowflies model should contain both additive and multiplicative stochastic noises. The suggested transformation technique is similar to that used in the deterministic case. The difference is hidden in the formulas for the exact solutions of the simplified boundary value problem for the stochastically perturbed MFE. The analysis is also based on the theory of stochastic delay differential equations.

Keywords: boundary value problems, population models, stochastic delay differential equations, stochastic partial differential equation

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Authors: Danilo López, Nelson Vera, Luis Pedraza

Abstract:

This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Edu Marcelina, Sule Sheidu A., Nsor Eunice

Abstract:

As open and distance education is gradually becoming an acceptable means of solving the problem of access in higher education, quality has now become one of the main concerns among institutions and stakeholders of open and distance learning (ODL) and the education sector in general. This study assessed the challenges of academic quality in the open and distance learning (ODL) education in Nigeria using Distance Learning Institute (DLI), University of Lagos and National Open University of Nigeria as a case. In carrying out the study, a descriptive survey research design was employed. A researcher-designed and validated questionnaire was used to elicit responses that translated to the quantitative data for this study. The sample comprised 665 students of the Distance Learning Institute (DLI), and National Open University of Nigeria (NOUN), carefully selected through the method of simple random sampling. Data collected from the study were analyzed using Chi-Square (X2) at 0.05 Level of significance. The results of the analysis revealed that; the use of ICT tools is a factor in ensuring quality in the Open and Distance Learning (ODL) operations; the quality of the materials made available to ODL students will determine the quality of education that will be received by the students; and the time scheduled for students for self-study, online lecturing/interaction and face to face study and the quality of education in Open and Distance Learning Institutions has a lot of impact on the quality of education the students receive. Based on the findings, a number of recommendations were made.

Keywords: open and distance learning, quality, ICT, face-to-face interaction

Procedia PDF Downloads 376